Robustness issues in Biometrics: Blind Source Separation and Cluster Ensembles Jugurta Montalvão

1. BioChaves Robustness issues in Biometrics: Blind Source Separation and Cluster Ensembles Jugurta Montalvão Universidade Federal de Sergipe – UFS Núcleo de Engenharia Elétrica - NEL E-mail: jmontalvao@ufs.br

2. BioChaves Outline: 1 – Robustness in (behavioural) Biometrics 2 – What is blind source separation? 3 – What if there are less signals than sources? 4 – Clustering is not a “piece of cake”! 5 – Cluster ensembles: an approach aiming at robustness 6 – Conclusions

3. BioChaves 1 - Robustness in (behavioural) Biometrics Example: Speaker recognition.

4. BioChaves 1 - Robustness in (behavioural) Biometrics Example: Speaker recognition. In everyday lives, people play the role of a powerful speaker recognition system! a) Enrollment: please, listen to this voice: b) Noiseless interrogation: Now, listen to these new recordings and decide whether it is or is not the same subject: a) b) c) Noisy interrogation: And now, can you still recognize the enrolled voice in this new recording?

5. BioChaves 1 - Robustness in (behavioural) Biometrics Question: Speaker recognition systems have existed since the 60’s… why are they not so popular as fingerprint based systems? Known drawback: Conventional speaker recognition systems perform poorly under noisy conditions. Robust Speaker Recognition Using Binary Time-Frequency MasksYang Shao; DeLiang WangAcoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference onVolume 1, Issue , 14-19 May 2006 Page(s):I – I “the use of binary masking represents a promising direction for robust speaker recognition “

6. BioChaves 1 - Robustness in (behavioural) Biometrics Algoritmos robustos de reconhecimento de voz aplicados a verificação de locutorTarciano Facco Pegoraro (mestrado, Unicamp, 2000)“conventional techniques were tested (spectral subtraction (SS), cepstral mean subtraction (CMN) and RAST A filtering) and a method for state duration modeling with temporal restrictions (MDE) that has recently been proposed” Robust speaker verification with state duration modelingSourceSpeech Communication Volume 38 ,  Issue 1  (September 2002) “This paper addresses the problem of state duration modeling in the Viterbi algorithm in a text-dependent speaker verification task. The results presented in this paper suggest that temporal constraints can lead to reductions of 10% and 20% in the error rates with signals corrupted by noise at SNR equal to 6 and 0 dB, respectively”

7. BioChaves 1 - Robustness in (behavioural) Biometrics Noise-Robust Speaker Recognition Using Subband Likelihoods and Reliable-Feature Selection Sungtak Kim, Mikyong Ji, and Hoirin Kim, ETRI Journal, vol.30, no.1, Feb. 2008. Auditory Sparse Representation for Robust Speaker Recognition Based on Tensor Structure Qiang Wu and Liqing Zhang, EURASIP Journal on Audio, Speech, and Music Processing Volume, 2008.

8. BioChaves 2 – What is blind source separation (BSS)? An illustrative example (adapted from http://www.cis.hut.fi/projects/ica/cocktail/cocktail_en.cgi) Single room with 3 microphones ICA Research at Helsinki University of Technology ICA: Independent component analysis (is usually utilized to solve the BSS problem)

9. BioChaves 2 – What is blind source separation (BSS)? Mixing (“wrapping” the original PDF) Demixing 9

10. BioChaves 3 - What if there are less signals than sources? Can you infer how many (signal) sources are in this recording ? (quite a tough problem for machines) Typical approaches: • Sparse component analysis (SCA or SPICA): relies on the assumption that sources are sparse (i.e. have a small number of non-zero values) or sparsely-represented in an appropriate domain (e.g., in the domain of the Fourier transform, wavelet transform, Gabor transform, etc.) • - Computational Auditory Scene Analysis (CASA ): typically extracts one source from a single channel of audio using heuristic psychological grouping rules (pattern matching). Separation based on frequency, loudness, pitch, timbre information, etc. M. S. Lewicki and T. J. Sejnowski. Coding time-varying signals using sparse, shift-invariant representations. Advances in Neural Information Processing Systems, 11:730–736, 1999 M. Zibulevsky and B. A. Pearlmutter. Blind source separation by sparse decomposition. Neural Computation, 13(4), 2001.

11. BioChaves 3 - What if there are less signals than sources? Bandoleon Speaker 1 Speaker 2 Illustration: a suitable N –D to 2-D mapping Hypothesis: There is at least one “suitable” mapping

12. BioChaves 3 - What if there are less signals than sources? - CASA (Computational Auditory Scene Analysis) Typically extracts one source from a single channel of audio using heuristic psychological grouping rules (pattern matching). Separation based on frequency, loudness, pitch, timbre information, etc.

13. BioChaves 4 – Clustering is not a “piece of cake”! to group similar objects together based on some notion of similarity, but… different methods/parameter choices -> varying clustering results Example: How many clusters do you see? Adapted from “Algorithms for Clustering Data”, A. K. Jain and R. C. Dubes, Prentice Hall, 1988.

14. BioChaves 4 – Clustering is not a “piece of cake”! And now? Worse yet: what about points in higher dimensional spaces...? Adapted from “Algorithms for Clustering Data”, A. K. Jain and R. C. Dubes, Prentice Hall, 1988.

15. BioChaves 5 – Cluster ensembles: an approach aiming at robustness - A. Strehl and J. Ghosh. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Machine Learning Research, 2002. - A. Topchy, A.K. Jain, and W. Punch. Clustering ensembles: Models of consensus and weak partition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005. - Ensemble clustering with voting active clusters, Kagan Tumer a,*, Adrian K. Agogino b, Pattern Recognition Letters, 2008. - Knowledge based Cluster Ensemble, Zhiwen Yu Zhongkai Deng Hau-San Wong Xing Wang , in: IEEE World Congress on Computational Intelligence, June 2008

16. BioChaves 5 – Cluster ensembles: an approach aiming at robustness What is it? Illustration: Please, three brave volunteers (waiting for raised hands...) 2 clusters or 1 cluster? Pattern “x”

17. BioChaves 6 – Conclusions • Biometrics robustness can be improved through blind source separation, as much as animals instinctively do (by hypothesis) • Blind source separation from single recordings may be properly tackled through clustering of “good” spatial/temporal signal representation • Robustness can be obtained by the use of cluster ensemble based approaches • Interesting subjects for research: 1) Finding “good” spatial/temporal signal representation (studying old and new meanings for the word “good” – e.g. Sparseness, statistical independence... ) 2) Adapting cluster ensemble based approaches to Biometrics applications.